Cracking the Code: A Deep Dive into Open Source Language Model Deployment and Inference

Dive into our event page, download presentations, rate the talk, and honor speakers with badges. Shape the future of our community events with your valuable feedback! https://datazen.top/krdf8 Embark on an enlightening journey with Senior Data Scientist Ali Oztas from EPAM Systems as he takes you through a deep dive into the realm of Open Source Language Model (LLM) deployment and inference. 🚀 In this comprehensive talk, we explore the intricate landscape of deploying LLMs across various environments, from GPU instances to local machines, shedding light on the crucial tradeoffs between open-source deployment, on-premises solutions, and closed-source services. 🖥️ Join us as we delve into the nuances of precision types and cutting-edge quantization techniques, including an in-depth examination of INT8-INT4 quantization and its impact on hardware usage. Discover the latest advancements in serving frameworks for LLMs, with a comparative analysis of key terms and performance metrics that will empower you to make informed decisions for your projects. ⚙️ Throughout the discussion, Ali Oztas addresses common queries such as the possibility of running LLMs on Raspberry Pi, the performance disparities between CPU and GPU, and the influence of quantization on hardware affordability and model results. Gain valuable insights into the production readiness of LLMs, supplemented by practical learning resources to enhance your understanding and proficiency in this rapidly evolving field. 💡 Whether you're a seasoned professional seeking to optimize LLM deployments or an enthusiastic learner eager to unlock the potential of these powerful models, this talk is your gateway to unraveling the secrets of harnessing LLMs effectively. Don't miss out on this opportunity to stay ahead of the curve in AI innovation! 🌐 #OpenSource #LanguageModels #AI #Deployment #Inference #Quantization #GPU #FrameworkComparison #RESTAPIs #EPAMSystems More interesting content: ► Community page: https://wearecommunity.io/communities... If you want to be the first to know about all our news, look here: ► Telegram: https://t.me/datazencommunity ► Instagram:   / data.zen.community   Do you dream of becoming a new speaker? We will be happy to help you! ► For cooperation: data.zen.community@gmail.com 00:00 - Introduction 01:52 - Meet Ali Oztas: Senior Data Scientist Exploring LLMs 02:19 - Dive into Language Model Deployment Contents 02:50 - Catalysts for Effective LLM Deployment 03:35 - Advantages of Open Source Language Models 06:40 - Maximizing Performance: Insights on GPU Hardware 07:27 - Understanding Precision Types for Language Models 09:05 - Optimizing Performance: Introduction to Quantization 10:57 - Harnessing Hardware Efficiency with Quantization 11:47 - Exploring Quantization Algorithms: GPTQ-Exllama, AWQ, and More 13:57 - Comparative Analysis of Quantization Techniques 15:50 - Essential Terms in Language Model Serving Frameworks 18:15 - Exploring Frameworks for LLM Deployment 24:43 - Key Notes on Language Model Frameworks 25:28 - Insights from Experimental Results 27:06 - Future Directions in Language Model Deployment 29:06 - Can LLMs Run on Raspberry Pi? Exploring Hardware Compatibility 29:40 - Performance Comparison: CPU vs GPU 30:28 - Unraveling Performance Disparities Across Frameworks 31:18 - Leveraging Quantization for Affordable Hardware Usage 31:58 - Impact of Quantization on Language Model Results 32:43 - Is LLaMa Ready for Production Deployment? 33:31 - Valuable Learning Resources for Language Model Deployment 34:19 - Wrapping Up: Closing Thoughts on LLM Deployment and Inference